El-Nagar Ahmad M, Khalifa Tarek R, El-Brawany Mohamed A, El-Bardini Mohammad, El-Araby Essam A G
Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menofia University, Menof, 32852, Egypt.
ISA Trans. 2022 Sep;128(Pt A):565-580. doi: 10.1016/j.isatra.2021.10.018. Epub 2021 Oct 20.
In this paper, a novel adaptive interval type-2 fuzzy controller (AIT2FC) is proposed for a class of nonlinear networked Wiener systems under packet dropout and time varying delay. The proposed AIT2FC compensates the negative effects of the packet dropout and time varying delay in both forward and feedback loops. The structure of the proposed AIT2FC has three parts, an adaptive interval type-2 Takagi-Sugeno (IT2TS) fuzzy controller, an IT2TS fuzzy Wiener model (IT2TS-FWM), and a time-varying delay and packet dropout compensator. The adaptive IT2TS fuzzy controller has a cascade connection; an IT2TS fuzzy controller followed by an inverse of an autoregressive moving average (IARMA) system. The nonlinear Wiener system is identified online by an IT2TS-FWM. An adaptive Smith predictor (ASP) is proposed to compensate the negative effects related to the time-varying delay. For each communication channel, the packet dropout is compensated via designing a compensation term in the stochastic Bernoulli approach. Based on the Lyapunov stability (LS) function, the parameters of the proposed AIT2FC are updated online. Also, the learning rates are updated online based on the LS function to avoid singularities and guarantee both the stability and fast convergence of the AIT2FC. The results conclude that the proposed controller is better than the other existing controllers.
本文针对一类存在数据包丢失和时变延迟的非线性网络化维纳系统，提出了一种新型自适应区间二型模糊控制器（AIT2FC）。所提出的AIT2FC补偿了前向和反馈回路中数据包丢失和时变延迟的负面影响。所提出的AIT2FC结构有三个部分，一个自适应区间二型高木 - 关野（IT2TS）模糊控制器、一个IT2TS模糊维纳模型（IT2TS - FWM）以及一个时变延迟和数据包丢失补偿器。自适应IT2TS模糊控制器具有级联连接；一个IT2TS模糊控制器后跟一个自回归滑动平均逆（IARMA）系统。非线性维纳系统由IT2TS - FWM在线识别。提出了一种自适应史密斯预估器（ASP）来补偿与时变延迟相关的负面影响。对于每个通信通道，通过在随机伯努利方法中设计一个补偿项来补偿数据包丢失。基于李雅普诺夫稳定性（LS）函数，所提出的AIT2FC的参数在线更新。此外，基于LS函数在线更新学习率，以避免奇异性并保证AIT2FC的稳定性和快速收敛。结果表明，所提出的控制器优于其他现有控制器。
